A graph-based explanatory model for room-based energy efficiency analysis based on BIM data

Frontiers in Built Environment

Built Environment

Mojgan Jadidi¹  Hamid Kiavarz²  Payam Esmaili³ 

[1] Department of Civil Engineering, Lassonde School of Engineering, York University, Toronto, ON, Canada;Lassonde School of Engineering, York University, Toronto, ON, Canada;Program Manager Willdan Energy Solutions, Maryland, MD, United States;

关键词: BIM;    graph machine learning;    energy efficiency consumption;    interpretable model BIM;    interpretable model;   

DOI  :  10.3389/fbuil.2023.1256921

 received in 2023-07-11, accepted in 2023-08-17,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: In recent years, the growing interest in building energy consumption and estimation has led to a wealth of energy data and Building Information Modelling (BIM), providing ample opportunities for data-driven algorithms to be widely applied in the building industry. However, despite promising accuracy in data-driven models for building energy estimation, they only consider building elements and their attributes independently and neglect the interconnected relationship of building elements. Also, Current data-driven models lack interpretability and are often treated as black boxes. As a result, the models cannot be fully trusted for engineering without reasoning the underlying mechanisms behind the estimation.Method: This paper emphasizes the potential of graph-based learning algorithms, specifically GraphSAGE, in utilizing the enriched semantic, geometry, and room topology information derived from BIM data. The aim is to identify critical zones within the building based on their energy consumption characteristics. Besides that, the paper proposed a GraphSAGE explainable model by adopting the SHAP with the proposed NE-GraphSAGE prediction model to make more transparency behind the data-driven models.Results and Discussion: Preliminary results demonstrate the potential to improve pre-construction and post-construction steps by identifying critical zones in buildings and identifying the parameters which affected the efficiency of the zones with low energy consumption.

【 授权许可】

Unknown   
Copyright © 2023 Kiavarz, Jadidi and Esmaili.